8. Windows Subsystem for Linux 2 (WSL 2)
WSL 2 speed comparison test WSL 1 on a Surface laptop
9
2.5x 4.7x 3.1x
9. VS Code Remote
Build, run, and debug your Linux applications
Directly from VS Code
Using WSL as the backend
10
10. Windows Subsystem for Linux 2 (WSL 2)
What’s new with the Windows Command Line
https://mybuild.techcommunity.microsoft.com/sessions/77293
The new Windows subsystem for Linux architecture: a deep dive
https://mybuild.techcommunity.microsoft.com/sessions/77003
11
11. The New Windows Terminal
12
https://www.youtube.com/watch?v=8gw0rXPMMPE
12. The New Windows Terminal
Multiple improvements
Tab support
Multiple shells
Windows Command Line, PowerShell, Bash, and others…
Modern design
New font, Emoji support, Smooth zooming
Extensible
Completely open source
https://github.com/microsoft/Terminal
Watch session
https://mybuild.techcommunity.microsoft.com/sessions/77004
13
13. React Native for Windows
Build native Windows apps with React
Open Source
https://github.com/Microsoft/react-native-windows/tree/master/vnext
Session
https://mybuild.techcommunity.microsoft.com/sessions/77007
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15. .NET Core 3.0 Preview 5
A lot of talk on:
Desktop support on .NET Core 3
gRPC support in ASP.NET Core 3
Blazor
https://dot.net/get-core3
16
18. XAMARIN / MONO.NET FRAMEWORK .NET
2014 Next2016
Many
.NETs
.NET
standard
.NET
.NET CORE
.NET STANDARD
19. .NET 5
INFRASTRUCTURE
.NET STANDARD
.NET—A unified platform
DESKTOP WEB CLOUD MOBILE GAMING IoT AI
WPF
Windows forms
UWP
ASP.NET Xamarin UnityAzure ARM32
ARM64
ML.NET
.NET for
Apache Spark
20. Distributed analytics engine for large datasets typically terabytes or petabytes
Currently available with Scala, Java, Python and R…
…but not .NET
22. Total execution time (seconds) for all 22 queries in the TPC-H benchmark (lower is better).
Data sourced from an internal run of the TPC-H benchmark, using warm execution on Ubuntu 16.04.
.NET for Apache Spark is designed for high
performance and performs better than python on
the TPC-H benchmark tpc.org/tpch.
The TPC-H benchmark consists of a suite of
business-oriented queries.
Learn more: dot.net/spark
406 433
375
.NET PYTHON SCALA
23. Custom ML made
easy with AutoML
Model Builder (a simple UI
tool) and CLI make it super
easy to build custom ML
Models.
Built for .NET
developers
Create custom ML models
using C# or F# without
having to leave the .NET
ecosystem.
Extended with
TensorFlow & more
Leverage other popular ML
frameworks (TensorFlow,
ONNX, Infer.NET, and more).
Trusted &
proven at scale
Use the same ML
framework which powers
Microsoft Office, Windows
and Azure
dot.net/ml
ML.NET 1.0
An open source and cross-platform machine learning framework
24. dot.net/ml
Product recommendation
Recommend products based on purchase history
using a matrix factorization algorithm.
Sentiment analysis
Analyze the sentiment of customer reviews
using a binary classification algorithm.
Price prediction
Predict taxi fares based on distance traveled
etc. using a regression algorithm.
Customer segmentation
Identify groups of customers with similar
profiles using a clustering algorithm.
Spam detection
Flag text messages as spam using a binary
classification algorithm.
Image classification
Classify images (e.g. broccoli vs pizza) using
a TensorFlow deep learning algorithm.
Sales forecasting
Forecast future sales for products using a
regression algorithm.
GitHub labeler
Suggest the GitHub label for new issues
using a multi-class classification algorithm.
Fraud detection
Detect fraudulent credit card transactions
using a binary classification algorithm.
26. Developer Tools
.NET Platform Overview and Roadmap
https://mybuild.techcommunity.microsoft.com/sessions/77031
All the Developer Things with Hanselman and Friends
https://mybuild.techcommunity.microsoft.com/sessions/77123
27
30. Text features
- Word ngrams
- unigrams
- bigrams
- trigrams …
- Character ngrams
- unigram …
- vocabulary size, idf, stop-words,
casing
- word embedding
- pretrained word
embeddings
- language?
- pretrained corpus?
- dimension?
- text similarity
- embedding-based
- ngram-based
Numerical features
- Discretization:
- k-means clustering
- n_clusters (2, 3, 4,..?)
- Equal sized bins
- n_bins (2, 3, 4,..?)
- Target encoding on bin-
categories.
- Scaling
- Normalization, percentile-based,
…
- Outlier removal
Categorical features
- one hot encoding
- Target encoding
- Cross-validation
- Regularization params
- Categoricals for trees
Time series forecasting features:
- lagged features
- Frequency detection
Timestamp features
- Day of week
- Day of month
- Day of year
- Month
- Hour
- Minute
- Holiday
- Quarter
31. Text features
- Word ngrams
- unigrams
- bigrams
- trigrams …
- Character ngrams
- unigram …
- vocabulary size, idf, stop-words,
casing
- word embedding
- pretrained word
embeddings
- language?
- pretrained corpus?
- dimension?
- text similarity
- embedding-based
- ngram-based
Numerical features
- Discretization:
- k-means clustering
- n_clusters (2, 3, 4,..?)
- Equal sized bins
- n_bins (2, 3, 4,..?)
- Target encoding on bin-
categories.
- Scaling
- Normalization, percentile-based,
…
- Outlier removal
Categorical features
- one hot encoding
- Target encoding
- Cross-validation
- Regularization params
- Categoricals for trees
Time series forecasting features:
- y- lagged features
- Frequency detection
Timestamp features
- Day of week
- Day of month
- Day of year
- Month
- Hour
- Minute
- Holiday
- Quarter
Without automated ML: hard, combinatorial explosion
With automated ML: easy, tailored to your dataset
32. xgboost alone: ~ 10^10 possible parameter configurations
Compute cost > 10^5 years
36. MLOps = ML + DEV + OPS
Experiment
Data Acquisition
Business Understanding
Initial Modeling
Develop
Modeling
Operate
Continuous Delivery
Data Feedback Loop
System + Model Monitoring
ML
+ Testing
Continuous Integration
Continuous Deployment
37. App developer
using Azure DevOps
Build appCollaborate Test app Release app Monitor app
Model reproducibility Model retrainingModel deploymentModel validation
Data scientist using
Azure Machine Learning
38. Code, dataset, and
environment versioning
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
39. Train model Validate
model
Deploy
model
Monitor
model
Retrain model
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
Azure DevOps integration
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning
40. Sessions
From Zero to AI Hero–Automatically generate ML models using Azure
Machine Learning service, Automated ML
https://mybuild.techcommunity.microsoft.com/sessions/76975
Breaking the Wall between Data Scientists and App Developers with MLOps
https://mybuild.techcommunity.microsoft.com/sessions/76973
41
44. GitHub
Azure Active Directory Support in GitHub Enterprise
GitHub Identity Support for Azure
45
45. Resources
All session videos and slide decks available here
https://mybuild.techcommunity.microsoft.com/sessions
46
46. Personal Recommendation
Inside Azure datacenter architecture with Mark Russinovich
My personal favorite
https://mybuild.techcommunity.microsoft.com/sessions/77002
47